Sofia Suvorova

Waveform libraries: Measures of effectiveness for radar scheduling

Our goal was to provide an overview of a circle of emerging ideas in the area of waveform scheduling for active radar. Principled scheduling of waveforms in radar and other active sensing modalities is motivated by the nonexistence of any single waveform that is ideal for all situations encountered in typical operational scenarios. This raises the possibility of achieving operationally significant performance gains through closed-loop waveform scheduling. In principle, the waveform transmitted in each epoch should be optimized with respect to a metric of desired performance using all information available from prior measurements in conjunction with models of scenario dynamics. In practice, the operational tempo of the system may preclude such on-the-fly waveform design, though further research into fast adaption of waveforms could possibly attenuate such obstacles in the future. The focus in this article has been on the use of predesigned libraries of waveforms from which the scheduler can select in lieu of undertaking a real-time design. Despite promising results, such as the performance gains shown in the tracking example presented here, many challenges remain to be addressed to bring the power of waveform scheduling to the level of maturity needed to manifest major impact as a standard component of civilian and military radar systems.

Clutter map and target tracking

Target tracking algorithms have to operate in an environment of uncertain measurement origin, in the presence of possibly non-detected target measurements as well as clutter measurements from unwanted scatterers. Integral part of expressions for data association probabilities is the estimate of clutter density. A priori knowledge of the clutter density may be derived in the form of the closed expression, whilst in the case of environmentally caused clutter; clutter measurement density has to be estimated using measurements from a number of scans, using a clutter map. This paper presents, as well as compares, three different clutter map estimators. The first is the classic clutter map estimator, which averages the number of measurements over a number of scans. This is a biased estimator of the inverse of clutter density. The second clutter map estimator uses spatial characteristics of the multi-dimensional Poisson process, and is termed spatial clutter map estimator. The third clutter map estimator uses temporal characteristics of the Poisson process, thus this estimator is termed temporal clutter map estimator. Both spatial and temporal clutter map produce an unbiased estimate of the inverse of clutter measurement density.

Waveform Libraries for Radar Tracking Applications: Maneuvering Targets

In this paper we extend the idea of adaptive waveform selection for radar target tracking to interacting multiple model (IMM) trackers to permit the modelling of maneuvering targets by allowing multiple possible dynamical models. We develop a one step ahead solution to the problem of waveform selection, which is designed to decrease dynamic model uncertainty for the target of interest. It is based on maximization of the expected information obtained about the dynamical model of the target from the next measurement. We also discuss the design of waveform libraries for target tracking applications.

Multitarget-multisensor tracking in an urban environment: a closed-loop approach

When compared to tracking airborne targets, tracking ground targets on urban terrains brings a new set of challenges. Target mobility is constrained by road networks, and the quality of measurements is affected by dense clutter, multipath, and limited line-of-sight. We investigate the integration of detection, signal processing, tracking, and scheduling by exploiting distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted radar waveform according to the scene conditions; and (3) motion model diversity by using a bank of parallel filters, each one matched to a different maneuvering model. Specifically, at each scan, the waveform that yields the minimum one-step-ahead error covariance matrix determinant is transmitted; the received signal is then matched-filtered, and quadratic curve fitting is applied to extract range and azimuth measurements that are input to the LMIPDA-VSIMM algorithm for data association and filtering. Monte Carlo simulations are used to demonstrate the effectiveness of the proposed system on a realistic urban scenario. A more traditional open-loop system, in which waveforms are scheduled on a round-robin fashion and with no other modes of diversity available, is used as a baseline for comparison. Simulation results show that our closed-loop system significantly outperforms the baseline system, presenting both a reduction on the number of lost tracks, and a reduction on the volume of the estimation uncertainty ellipse. The interdisciplinary nature of this work highlights the challenges involved in designing a closed-loop active sensing platform for next-generation urban tracking systems.

Doppler Resilience, Reed-Müller Codes and Complementary waveforms

While the use of complementary waveforms has been considered as a technique for providing essentially perfect range sidelobe performance in radar systems, its lack of resilience to Doppler is often cited as a reason not to deploy it. This work describes and examines techniques both for providing Doppler resilience as well as tailoring Doppler performance to specific aims. The Doppler performance can be varied by suitably changing the order of transmission of multiple sets of complementary waveforms. We propose a method which improves Doppler performance significantly in specific Doppler ranges by arranging the transmission of multiple copies of complementary waveforms according to a suitable choice from the first order Reed-Muller codes. We provide both a theoretical analysis and computer simulations of the Doppler response of waveform sequences constructed in this way.

Adaptive modelling of sea clutter and detection of small targets in heavy clutter

This paper describes and compares three methods for detection of low RCS radar targets in heavy sea clutter using a high PRF radar. Two methods are based on auto-regressive processes, one on Karhunen-Loeve decomposition. Experiments have been performed on DSTO sea clutter dataset using these methods and the results are presented in this paper.

A new complementary waveform technique for radar signals

A new phase coding technique for radar signals is introduced which uses novel complementary waveforms constructed to have optimal sidelobe performance. The waveforms are constructed using a modification of the Prometheus orthonormal set (PONS) technique. An advantage of a PONS matrix is that it allows for many complementary pairs of waveforms to choose from as well as allowing for multiple pairs to be used simultaneously. It is shown that sets of waveforms which are complementary in quartets can also be applied for more flexibility. Results showing improved ambiguity properties versus other radar waveform coding techniques are given.

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance☆

Abstract Suvorova, S., and Schroeder, J., Automated Target Recognition Using the Karhunen–Loeve Transform with Invariance, Digital Signal Processing 12 (2002) 295–306 In this paper we present an automated target recognition (ATR) algorithm, based on the Karhunen–Loeve transform with rigid motion invariance. The ATR algorithm aims reduce the storage space and the processing time by comparison with a template-matching algorithm without the loss of performance. The algorithm has been tested on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset both for training and for classification.

An overview of Renyi Entropy and some potential applications

We introduce Renyi Entropy in this paper and review the basic associated properties that differentiate Renyi Entropy from Shannon Entropy. Theoretic and simulation examples are used to illustrate the differences between the two entropies. We suggest several potential applications of Renyi Entropy to such areas as spectral estimation and pattern recognition.

Sensor scheduling for target tracking in large multistatic sonobuoy fields

Sonobuoy fields, consisting of many distributed emitter and receiver sonar sensors on buoys, are used to seek and track underwater targets in a defined search area. A sensor scheduling algorithm is required in order to optimise tracking performance by selecting which emitter sonobuoy should transmit in each time interval, and which waveform it should use. In this paper we describe a new long term sensor scheduling algorithm for sonobuoy fields, called the continuous probability states algorithm. This algorithm reduces the scheduling search space by keeping track of the probability that a target is undetected, rather than modelling all possible detection outcomes, which reduces the computation complexity of the algorithm. It is shown that this approach results in high quality tracking for multiple targets in a simulated sonobuoy field.